Learn in one article: Why does the Big Model Agent Framework (A2A) use JSON-RPC 2.0?

Introduction: With the booming development of AI technology, large model agents (such as GPT-4, Claude, etc.) need to frequently interact with external tools, other agents, and even cloud services. How to complete these communications efficiently and securely? The answer lies in  JSON-RPC 2.0  , a seemingly "old" but rejuvenated protocol. This article will analyze why JSON-RPC 2.0 has become the "darling" of large model agent transmission protocols and reveal its core advantages.

1. What is JSON-RPC 2.0?

JSON-RPC 2.0 is a lightweight remote procedure call (RPC) protocol based on JSON, which was created in 2010. It allows clients to call remote services just like calling local functions through a simple request-response mechanism. For example:

{ "jsonrpc" :  "2.0" ,  "method" :  "sum" ,  "params" : [ 1 , 2 , 3 ],  "id" :  1 }

The server returns:

{ "jsonrpc" :  "2.0" ,  "result" :  6 ,  "id" :  1 }

This concise structure makes it an ideal choice for cross-language and cross-platform communication.

2. Why do large model agents prefer JSON-RPC 2.0?

1. Lightweight and efficient, suitable for high-concurrency scenarios

• Small data volume : The JSON format is naturally compact and reduces transmission overhead by more than 30% compared to protocols such as XML. It is suitable for bandwidth-sensitive AI tasks (such as real-time risk control and streaming output).
• Asynchronous support : Supports non-blocking calls. AI Agent can process multiple requests at the same time to avoid blocking the reasoning process due to waiting for responses (such as the asynchronous engine in the MCP protocol).

2. Flexible compatibility, no pressure on cross-platform

• Transport independence : The protocol is not bound to a specific transport layer and can be seamlessly adapted to HTTP, WebSocket, TCP, and even inter-process communication. For example, the MCP protocol is based on JSON-RPC 2.0 and supports both local STDIO and remote HTTP/SSE communication.
• Language neutrality : Almost all programming languages ​​support JSON parsing, making it easy for AI developers to quickly integrate (such as Python, Java, and Rust).

3. Standardized interfaces to reduce development costs

• Strong type definition : Standardize interface parameters and return values ​​through JSON Schema to reduce ambiguity when AI parses data (such as millisecond-level transaction data synchronization in financial scenarios).
• Batch call and notification mechanism :
• Batch requests : A single communication can contain multiple calls to improve efficiency (such as calling the demand forecasting and warehouse scheduling interfaces simultaneously in supply chain optimization).
• Notification : A one-way operation that does not require waiting for a response, suitable for scenarios such as logging and progress push.

4. Controllable security, adaptable to complex scenarios

• Error standardization : built-in error codes (such as -32601 means "method not found"), which makes it easier for the AI ​​system to quickly locate the problem.
• Integration with security protocols : MCP and A2A protocols overlay OAuth authentication, data encryption and other mechanisms on top of JSON-RPC to ensure zero-trust security for inter-Agent communication.

3. Practical Application Scenarios

1. Tool calling : AI calls database query, file cleaning and other tools through the MCP protocol without writing customized code.
2. Multi-agent collaboration : In e-commerce scenarios, customer service agents use the A2A protocol to query inventory agents in real time to solve user order problems.
3. Streaming data processing : Combined with SSE (Server-Sent Events) to achieve real-time progress feedback, suitable for long-term tasks (such as genetic data analysis).

IV. Future Trends

JSON-RPC 2.0 is still evolving:

• Performance optimization : Combined with binary encoding (such as CBOR) to improve efficiency in high-throughput scenarios.
• Protocol integration : MCP may introduce the A2A Agent Card mechanism to enhance dynamic service discovery capabilities.

Conclusion JSON-RPC 2.0 has become an "invisible bridge" for large-model agent communication due to its lightweight, flexible, and standardized features. Whether it is tool calling or multi-agent collaboration, it silently supports the prosperity of the AI ​​ecosystem behind the scenes.